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Rehabilitation Driven Optimized YOLOv11 Model for Medical X-Ray Fracture Detection.

Wenqi Zhang1, Shijun Ji2

  • 1School of Nursing, Jilin University, Changchun 130025, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study introduces an improved YOLOv11n model for precise fracture detection in X-ray images. The enhanced model significantly boosts accuracy and localization capabilities, aiding faster medical diagnosis.

Keywords:
X-rayYOLOv11 modeldata augmentationfractureobject detection

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Accurate fracture identification from X-ray images is vital for effective medical treatment.
  • Existing fracture detection models often exhibit limitations in localization accuracy and overall performance.
  • Scarcity of medical imaging data can lead to overfitting in deep learning models.

Purpose of the Study:

  • To develop a precise medical X-ray fracture detection model with enhanced localization capabilities.
  • To improve upon the You Only Look Once version 11 nano (YOLOv11n) model for fracture detection.
  • To address challenges of false localization and poor accuracy in current fracture identification systems.

Main Methods:

  • A novel data augmentation technique (random rotation, translation, flipping, content recognition padding) was employed to expand the dataset and mitigate overfitting.
  • A Bone-Multi-Scale Convolutional Attention (Bone-MSCA) module, integrating multi-directional convolution, deformable convolution, edge enhancement, and channel attention, was incorporated into the backbone network.
  • The Focal mechanism was combined with Smoothed Intersection over Union (Focal-SIoU) as the loss function to improve sensitivity to small fracture areas and optimize direction perception.

Main Results:

  • The improved model demonstrated superior performance compared to other mainstream single-object detection models.
  • Key performance metrics showed significant increases: detection accuracy improved by 4.33% to 93.56%, recall rate by 0.92% to 86.29%, F1-Score by 2.52% to 89.78%, and mean Average Precision 50 by 1.24% to 92.88% compared to the original YOLOv11n.
  • Visualizations confirmed the model's high accuracy and precise localization abilities in detecting fractures on medical X-ray images.

Conclusions:

  • The proposed enhanced YOLOv11n model with the Bone-MSCA module and Focal-SIoU loss function offers a significant advancement in medical X-ray fracture detection.
  • The data augmentation strategy effectively addresses the issue of limited medical imaging data, reducing overfitting.
  • The model's improved accuracy and localization capabilities hold promise for more reliable and timely diagnosis of fractures.